PrivySharing: A Blockchain-based Framework for Integrity and

Privacy-preserving Data Sharing in Smart Cities

Imran Makhdoom

1 a

, Ian Zhou

1 b

, Mehran Abolhasan

1 c

, Justin Lipman

1 d

and Wei Ni

2 e

University of Technology Sydney, New South Wales, Australia

Data61-CSIRO, Marsﬁeld, New South Wales, Australia

Keywords:

Internet of Things, Smart City, Security and Privacy, Blockchain, EU GDPR Compliance.

Abstract:

The ubiquitous use of Internet of Things (IoT) ranges from industrial control systems to e-Health, e-commerce,

smart cities, supply chain management, smart cars, cyber-physical systems and a lot more. However, the

data collected and processed by IoT systems especially the ones with centralized control are vulnerable to

availability, integrity, and privacy threats. Hence, we present “PrivySharing,” a blockchain-based innovative

framework for integrity and privacy-preserving IoT data sharing in a smart city environment. The proposed

scheme is distinct from existing technologies on many aspects. The data privacy is preserved by dividing

the blockchain network into various channels, where every channel processes a speciﬁc type of data such as

health, smart car, smart energy or ﬁnancial data. Moreover, access to user data within a channel is controlled

by embedding access control rules in the smart contracts. In addition, users' data within a channel is further

isolated and secured by using private data collection. Likewise, the REST API that enables clients to interact

with the blockchain network has dual security in the form of an API Key and OAuth 2.0. The proposed

solution also conforms to some of the signiﬁcant requirements outlined in the European Union General Data

Protection Regulation. Lastly, we present a system of reward in the form of a digital token “PrivyCoin” for

the users for sharing their data with the stakeholders/third parties.

1 INTRODUCTION

IoT-based services are expected to connect 30 bil-

lion devices by 2020 (Lund et al., 2014). Use of

IoT technologies will not only improve the quality of

life of people but also contribute to the world econ-

omy. However, at the same time global urban pop-

ulation is also predicted to reach 5 billion by 2030.

This rapid urbanization demands effective, and op-

timum use of city resources as well as smart gover-

nance and efﬁcient service delivery (Moustaka et al.,

2018; Zhang et al., 2017). It is believed that the solu-

tion to the rapid urbanization problems lies in creat-

ing smart cities that utilize IoT technologies to mon-

itor the physical world in real-time and provide in-

telligent services. These services may include eToll,

smart parking (Zhang et al., 2017), smart health, and

https://orcid.org/ 0000-0002-6205-5897

https://orcid.org/0000-0002-7154-4561

https://orcid.org/0000-0002-4282-6666

https://orcid.org/0000-0003-2877-1168

https://orcid.org/0000-0002-4933-594X

police assistance (Moustaka et al., 2018).

Concurrently, IoT devices are vulnerable to a vast

number of security and privacy attacks (Makhdoom

et al., 2018). Although, these threats are known to

the manufacturers but security in IoT devices is either

neglected or treated as an afterthought (Wurm et al.,

2016). Sequel to this, a smart city network also suffers

from numerous security and privacy issues (Moustaka

et al., 2018; Bartoli et al., 2011), such as threats to pri-

vacy, integrity, and availability of user data, false data

injection (Zhang et al., 2017), vulnerability to Sybil

Attack (Cui et al., 2018), and single point of failure

due to centralized control.

If we look at Figure-1, the user data collected by

numerous sensors is stored and processed by various

OSN (Online Social Networks), smart city control

centre or various other smart city components such

as Intelligent Transportation Systems (ITS), health

emergency response, ﬁre and rescue etc., These com-

ponents with mostly centralized control process user

data for provision of various services to the users and

third parties. Although such a centralized control may

look effective from the outside, yet it has some signif-

Makhdoom, I., Zhou, I., Abolhasan, M., Lipman, J. and Ni, W.

PrivySharing: A Blockchain-based Framework for Integrity and Privacy-preserving Data Sharing in Smart Cities.

DOI: 10.5220/0007829803630371

In Proceedings of the 16th International Joint Conference on e-Business and Telecommunications (ICETE 2019), pages 363-371

ISBN: 978-989-758-378-0

363

Figure 1: Issues in the smart city environment.

icant security concerns.

Centralized control is subject to a single point of

failure in case of a cyber-attack or other technical mal-

functions (Puthal et al., 2016). Moreover, it also has

trust issues, as the users have to put their trust in the

entity that is handling their data. Hence, users have

no control over their data assets. Further concerns for

user data include: Users do not know where their data

is stored? Who has access to it? Is their any unautho-

rized disclosure to the third parties? These concerns

are very realistic as the disclosure of personal data

leakage concerning 87 million users by Facebook Inc.

in April 2018 (Sara and Michael, 2018) and a bug in

Google Plus (Sara, 2018) that resulted in the exposure

of personal information of approx 500,000 users is a

candid example of one of cloud/OSN vulnerabilities.

Moreover, any smart city application is believed

to store, process and analyze users' data. Hence, ev-

ery security solution developed for a smart city envi-

ronment must comply with the undermentioned key

requirements of European Union General Data Pro-

tection Regulation (EU GDPR) (GDPR, 2018) while

handling users' data:

• Personal data should be gathered and processed

only with the consent of the data owner based on

a contract.

• Any technology dependent on user data must pre-

serve user privacy by design.

• The owner of data has the right to know, as to who

has access to his data?

• User data be erased immediately once it is no

longer required.

• The system should be transparent and must log all

the activities concerning user data.

As far as IoT security is concerned, though

blockchain was initially conceived as a ﬁnancial

transaction (TX) protocol in the form of Bitcoin, but

due to its cryptographic security beneﬁts, researchers

and security analysts are focusing on the blockchain

to resolve security and privacy issues of IoT. Hence,

we believe that a carefully selected blockchain tech-

nology with an insightful business network design can

resolve most of the data integrity and privacy issues of

a smart city.

1.1 Related Work

Security researchers around the world are develop-

ing and investigating ingenious ways to implement

blockchain in the IoT environment. These use cases

aim to take advantage of the inherent beneﬁts of the

blockchain such as decentralized control, immutabil-

ity, cryptographic security, fault tolerance, and capa-

bility to run smart contracts. Recently, researchers

(Michelin et al., 2018) presented a blockchain-based

data sharing framework for a smart city environment.

The framework called "SpeedyChain" focuses on re-

ducing the TX settlement time for real-time applica-

tions such as smart cars and also aims to ensure user

privacy. Moreover, it ensures data integrity, tamper-

resistance, and non-repudiation that are some of the

intrinsic beneﬁts of the blockchain. In another work,

Pradip Kumar and Jong Hyuk proposed a Software

Deﬁned Networking (SDN) and blockchain-based hy-

brid network architecture for a smart city (Sharma

and Park, 2018). The proposed architecture addresses

usual smart city issues including high TX latency,

security and privacy, bandwidth bottlenecks, and re-

quirement of high computational resources. Authors

claim that the proposed model limits the effects of

SECRYPT 2019 - 16th International Conference on Security and Cryptography

364

node compromise to the local area.

Additionally, researchers (Rahman et al., 2019)

proposed smart contract based sharing economy

services in a smart city. The proposed model

uses Artiﬁcial Intelligence (AI) for data analytics

and uses blockchain to store the results. Simi-

larly, Biswas and Muthukkumarasamy (Biswas and

Muthukkumarasamy, 2016) presented an overview

of a blockchain-based security framework for secure

communication between smart city entities. However,

it is not clear that which blockchain platform, and

consensus protocol is used in the smart city applica-

tion?

In another endeavor (Kountché et al., 2017;

Haidar et al., 2017), security researchers have pro-

posed solutions to address various user privacy is-

sues in ITS. Nonetheless, they do not cater for the

challenges of the smart cities such as trustless data

sharing among multiple organizations. Similarly, Ali

Dorri and Raja Jurdak proposed a secure, private and

lightweight architecture of a blockchain-based smart

home application (Dorri et al., 2016; Dorri et al.,

2017). It aims to solve certain blockchain issues such

as computational intensiveness, latency in TX conﬁr-

mation and energy consumption. However, there are

many security concerns that need further explanation

(Makhdoom et al., 2018b). Likewise, authors (Bu-

terin et al., 2014) proposed an Ethereum Blockchain

based mechanism to manage IoT devices (Huh et al.,

2017). Nevertheless, Ethereum Blockchain does not

provide data privacy. Correspondingly, Yu Zhang

and Jiangtao Wen proposed an Ethereum Blockchain

based decentralized electronic business model for the

IoT (Zhang and Wen, 2016). Whereas, the proposed

solution mostly focused on the working of the e-

business model, and thus lacking technical aspects.

Though the research work discussed above has

certainly made some signiﬁcant contributions towards

blockchain and IoT domain. However, still, there

are many open issues such as preserving data pri-

vacy in a smart city environment, user-deﬁned ﬁne-

grained access control, fast TX settlement, users'

right to forget (concerning data deletion), an in-

centive for users to share their data, etc. There-

fore, to ﬁll the respective research gaps, we pro-

pose “PrivySharing,” a blockchain-based secure and

privacy-preserving framework. The experimental re-

sults have shown that a carefully designed blockchain

solution can ensure user data privacy and integrity in

various network settings as per the wishes of the data

owner. It also effectively protects against false data

injection and Sybil Attacks. Moreover, PrivySharing

complies with some of the signiﬁcant data security

and privacy requirements of the European Union Gen-

eral Data Protection Regulation (EU GDPR). The ma-

jor contributions of this paper are:

1. Protection against most of the smart city threats

concerning user data integrity and privacy.

2. Compliance with some of the essential require-

ments of EU GDPR.

3. A blockchain-based solution providing the “right

to forget” concerning user data.

4. A scalable (concerning blockchain size), secure

and an efﬁcient (in terms of energy consump-

tion and computational requirements) data sharing

framework.

5. User-deﬁned ﬁne-grained access control to his/her

data.

6. Providing a transparent and auditable network op-

eration and simultaneously preserving user data

privacy.

7. Secure client access to the blockchain network

through a REST API.

8. A reward system for the users for sharing their

data with the stakeholders/third parties.

1.2 Organization of the Paper

The paper is organized into four sections. Section-

2 presents the detailed architecture, working, reward

mechanism and security analysis of “PrivySharing.”

Experimental results including Access Control List

(ACL) rules and some limitations of the proposed so-

lution are illustrated in Section-3. Finally, the paper

is concluded in Section-4, with a gist of future work.

2 PRIVYSHARING:

BLOCKCHAIN-BASED SECURE

DATA SHARING

By leveraging data integrity and smart contract fea-

tures of the blockchain, various operations in a

smart city environment can be securely and au-

tonomously performed. Moreover, blockchain also

protects against the adverse effects of server hack-

ing and falsiﬁcation/modiﬁcation of permissions (Cui

et al., 2018). No doubt, people in a smart city envi-

ronment feel safe when they have the assurance that

their personal and sensitive data collected by various

devices is fully protected and they have the control

over it (Mazhelis et al., 2016). Such assurance can

only be provided by none other than the blockchain

technology.

PrivySharing: A Blockchain-based Framework for Integrity and Privacy-preserving Data Sharing in Smart Cities

365

Figure 2: Network participants.

Table 1: List of assets.

Data Types Assets

Health Data

Health Alert (Heart rate, blood sugar, blood alcohol

etc.)

Full Health History

Insurance Cover

Health Payment Claims

Type of Disease

Current Disease History

Smart Car Data

GPS Data

Accident Alert

Damage Assessment

Servicing and Auto Payments

Smart Meter Data

Line Status

Units Consumed and Bill

Consumption Pattern

Surveillance Data

Equipment Status and Servicing

Security Breach Alert

CCTV Recording

Financial Transactions

Income

Expenses

Tax

2.1 Smart City Scenario

Let's assume that Alice is living in a smart city where

every aspect of her life is being monitored and con-

trolled through numerous sensors and smart devices.

The critical aspects include personal health, smart liv-

ing, smart car, performance of security apparatus, and

ﬁnancial TXs to keep the services running. For better

understanding, we have formulated a list of numerous

assets (associated with a speciﬁc type of data) that Al-

ice owns (as shown in Table-1). Based on these assets,

Alice can easily decide as to which stakeholder/third-

party needs access to which asset? and what oper-

ation he can perform on that asset? Such a distinc-

tion among the stakeholders/third-parties further as-

sists Alice to plan and control the access to her data.

To implement above mentioned smart city use

case we have used Hyperledger-Fabric (Androulaki

et al., 2018) as the underlying blockchain platform

due to its effective data security and privacy pre-

serving capabilities as compared to other blockchain

platforms (Makhdoom et al., 2018a; Makhdoom

et al., 2018b). The key feature that distinguishes

Hyperledger-Fabric from other blockchain technolo-

gies is that the blockchain ledger consists of two dis-

tinct but related parts, i.e., a blockchain to log the TXs

and a world state (a database such as CouchDB, and

LevelDB) to keep track of the ledger states.

2.2 Basic Terminologies

Some terminologies speciﬁc to Hyperledger-Fabric

are:

• Committing Peers. Every peer node in the

Hyperledger-Fabric blockchain is a committing

peer. However, a Committing Peer does not have a

smart contract installed. It just validates and com-

mits a new block of TXs sent by the Ordering Ser-

vice to its copy of the ledger.

• Endorsing Peers. These are special committing

peers with the capability to run the smart con-

tracts. They prepare, sign and endorse the re-

sponses to the TX proposals sent by the clients,

in line with the endorsement policy of the respec-

tive channel (Ch).

• Ordering Service. It is a collection of some peer

nodes that arrange the new TXs in a block and

then broadcast that block to all the peers of the

concerned Ch.

• Membership Service Provider (MSP). On the

one hand, Certiﬁcate Authorities (CAs) issue

X.509 certiﬁcates to the network entities, while,

on the other, an MSP states that which peer nodes

are members of which organization. Different

MSPs can be used to represent various organiza-

tions or multiple groups within an organization.

Usually, the MSPs are deﬁned at the network, Ch

and local/peer level.

SECRYPT 2019 - 16th International Conference on Security and Cryptography

366

Figure 3: Smart city blockchain-network architecture.

2.3 Network Architecture

As shown in Figure-2, we have designed a smart city

blockchain network comprising eleven organizations

and their associated peer nodes. Keeping in view

the sharing of different categories of users' data with

different stakeholders and the requirement to ensure

user data privacy and security, the blockchain network

shown in Figure-3 comprises ﬁve different data Chs.

Where, Ch1 is used for the sharing of users' health

data, and similarly, Ch2 for smart transportation, Ch3

for smart energy, Ch4 for smart security and Ch5 han-

dles ﬁnancial data. Hence, these Chs serve to pre-

serve the privacy of user data by securely sharing a

particular type of data with authorized entities only.

The network is initiated by organization-1 (O1), and

is governed by the policy rules speciﬁed in the net-

work conﬁguration (NC). NC also controls access to

the smart city network. Similarly, every Ch is reg-

ulated by the policy rules speciﬁed in the respective

Channel Conﬁguration (CC). In this setting, Ch1 is

under the control of O2 and O5 and is governed by

CC1. Correspondingly, Ch2 is regulated by CC2, and

so on.

The CC is essential for Ch security, e.g., if the

client application (clientApp) wants to access a SC

on P1, then P1 consults its copy of CC1 to determine

the operations that clientApp can perform. More-

over, there is a separate ledger for every Ch, and all

the peer nodes have to maintain a copy of the ledger

concerning every Ch they are member of. Data in a

Ch is isolated from the rest of the network includ-

ing other Chs. Another important aspect of smart city

blockchain network is the ordering service (ODS),

which is common to all the Chs. Each node in the

ordering service keeps a record of every Ch created

through NC. Regarding CAs, every organization in

the network can have its own CA. But there is one

Root CA (RCA) in the network to establish the root of

trust. As a Proof of Concept (PoC) for PrivySharing,

we are using Hyperledger-Fabric RCA to issue X.509

certiﬁcates to all the network entities. These certiﬁ-

cates serve to authenticate the network entities and to

digitally sign the client application TX proposals and

smart contract TX responses. A user accesses the net-

work through a clientApp with a speciﬁc X.509 ID,

using a smart contract (SC). It is imperative to men-

tion that only the endorsing peers can see the SC logic

as they have to run the users' TX proposals to prepare

the responses.

To ensure the privacy of critical user data within

a Ch, we adopted a methodology of “Private Data

Collection,” in which the critical private data is sent

directly to the authorized organizations/stakeholders

only. This data is stored in a private database (a.k.a

sideDB) on the authorized nodes, and only the hash

of this data is processed, i.e., endorsed, ordered and

written to the ledgers of every peer on the Ch. The

hash of the data serves as an evidence of the TX, and

it also helps in the validation of the world state. An

important data security feature here is that the order-

ing nodes do not see the private data.

Another important feature of the proposed net-

work architecture is the use of Membership Service

Provider (MSP) at various levels such as network, Ch

and local/peer. The network MSP (NMSP) deﬁnes the

members of the network and their admin rights. Addi-

tionally, an NMSP also deﬁnes that which RCAs/CAs

PrivySharing: A Blockchain-based Framework for Integrity and Privacy-preserving Data Sharing in Smart Cities

367

are trusted. On the other hand, the Ch MSPs (CMSP)

outline admin and participatory rights at the Ch level.

A use case for the CMSP is that, e.g., an admin of an

organization wants to instantiate a SC on Ch1, then

by looking at the CMSP the other Ch members can

verify that whether that admin is a part of a speciﬁc

organization or not and whether he is authorized to

instantiate the SC on Ch1 or not.

Similarly, a local MSP (LMSP) is deﬁned for ev-

ery client-node/peer. The LMSP associates a peer

with its organization. Here a question may arise that,

what is the difference between CC and a CMSP? A

CC contains the policies that govern that Ch, i.e.,

which all organizations regulate that Ch? Who can

add new members to the Ch? Whereas, a CMSP es-

tablishes the linkage between the nodes and their re-

spective organizations.

Another question may arise that what advantages

do we get by using multiple Chs for different data

types? There are two aspects to this selection; one

is scalability and second is increased privacy of user

data. From the scalability point of view, if there is

only one Ch for all data types, then it means that

all the users will have to store the ledger compris-

ing all TXs that are not even related to them. Hence,

the ledger size will be increasing rapidly thus putting

more strain on storage resources of all the users/peers.

Whereas, in the case of “PrivySharing,” the users will

maintain a ledger that stores only that data which

concerns all the users of that particular Ch. As far

as the privacy of user data is concerned, a data spe-

ciﬁc Ch shared only by some of the stakeholders pro-

vides more privacy than a single Ch comprising all the

stakeholders sharing multiple data types.

2.4 Reward Mechanism

PrivySharing incentivizes the users to share their data

with other users, stakeholders or third parties by re-

warding them with a local digital token named “Privy-

Coin.” The users get the incentive based on the num-

ber of days their data is shared with the stakehold-

ers/third parties, as soon as the TX for data sharing is

committed. In this context, if a stakeholder does not

have requisite coins in their account, the TX will fail

(shown in Figure-4). The coins are issued to the users

and the stakeholders by the network admin only.

2.5 Security Analysis

The security, being the core objective of this work has

been assessed at every level of the network operation.

The key aspects are as under:

When the blockchain network is ﬁrst created, all

Figure 4: Error for not having enough coins.

the peers and orderer organizations are issued with

certiﬁcates from respective RCA, or other trusted

CAs. Then, a connection proﬁle is created for all

the network entities including Chs, ODS, organiza-

tions, peers, and CAs. The connection proﬁle de-

ﬁnes the complete blockchain network setup. The

key point here is that no other peer (with the in-

tention of endorsing the TXs on a Ch) can join the

network if it is not deﬁned in the connection pro-

ﬁle. It is clariﬁed that by peers, we mean commit-

ting, endorsing or ODS peer nodes that maintain the

blockchain network. Whereas, the users/clients ac-

cess the blockchain network through REST API or

clientApps.

To deploy the business network model (PrivyShar-

ing in this case) comprising asset deﬁnitions, TX

logic, and ACL rules, on the blockchain, the admin of

responsible organization (O1 in this scenario) requires

a Business Network Card (BNC). BNC is created us-

ing the connection proﬁle of the organization and the

valid public and private key for that admin issued by

the authorized CA, as deﬁned in the connection pro-

ﬁle.

The TXs initiated by the clientApps on a speciﬁc

Ch are endorsed as per the endorsement policy de-

ﬁned before the start of the business network. The

endorsement policy may include, e.g., which all peers

(with endorsing ability) are required to endorse a TX

on a Ch concerning health data? Correspondingly, a

TX is considered valid, only if the responses of all

the required endorsing peers are same. Hence, only a

valid TX will update the world state.

Another security feature is that before the start of

the business network on the blockchain, business net-

work admins have to be deﬁned and issued with the

certiﬁcates by the respective organizations with ad-

min rights on a Ch. These certiﬁcates are later used

to create the BNCs for the said admins to access the

business network. Without a valid BNC, no one can

add participants (clients/peers) for an organization.

Moreover, every new client/peer added under an or-

SECRYPT 2019 - 16th International Conference on Security and Cryptography

368

Figure 5: Validation of assets access control.

ganization is also issued with an ID by the respective

CA with the approval of the business network admin.

These IDs are further used to control access to the

users' proﬁle and assets as per the ACL rules deﬁned

for the speciﬁc Ch.

Access to the REST API is secured using the

API key which is required to launch the REST

API. In addition to the API Key, OAuth 2.0 au-

thorization protocol is also deployed to authenticate

the users/stakeholders, authorize access to the REST

server instance, and allow the stakeholders/users to

interact with the business network deployed on the

blockchain. Furthermore, due to the distributed nature

of the smart contracts, the integrity of any business

network deployed on the blockchain is guaranteed.

Similarly, it also protects against hacking of servers,

where, the attackers can change the policy rules, es-

calate access rights, etc. Correspondingly, protection

against application and web vulnerabilities can also

be guaranteed with high probability, as any change in

the smart contract requires installing and instantiat-

ing a new version of the contract on all the endorsing

peers. However, it can not be done discretely. Addi-

tionally, due to a distinction between blockchain and

the world state, an auditable log of TXs and events is

maintained without compromising the privacy of the

users' data.

Considering decentralization aspect, the use of

a dedicated trusted CA, a blockchain admin, and a

business network admin by every organization in the

blockchain network, provides some degree of decen-

tralization as compared to all the admin rights resting

with a single organization.

3 EXPERIMENTAL RESULTS

To validate the security effectiveness of the proposed

solution, we developed a business network model

of health data sharing in a smart city environment

(PrivySharing), i.e., the operation of Ch1 (as shown

in Figure-3), in Hyperledger Composer-Playground

version 0.20. As a PoC, we deployed the business

network architecture of PrivySharing on Hyperledger

Fabric ver 1.2 with one Ch, four committing peers

with endorsing powers and two ordering nodes. We

have used CouchDB as a world state because of its

support for rich queries. It is also validated that access

to users' health data assets is effectively regulated by

numerous ACL rules.

3.1 ACL Rules

These rules enforce that the data asset owners have

access to their assets only, i.e., no user can see data

assets of any other user, and only the data own-

ers can initiate a TX to share their data assets with

other users/stakeholders. Similarly, the data owner

has the right to revoke the sharing of his assets, and

he can also delete his assets when no longer re-

quired without affecting the TX history stored on the

blockchain. Moreover, as all the TXs are recorded

on the blockchain, hence, to increase privacy, a

data owner can see the TX history concerning his

own assets only. Additionally, the valid users can

read and update their own proﬁles only, and other

users/stakeholders cannot see each other's proﬁle.

Users can also delegate the stakeholders to create as-

sets for the respective users. E.g., Alice (P5) delegates

PrivySharing: A Blockchain-based Framework for Integrity and Privacy-preserving Data Sharing in Smart Cities

369

her primary medical centre (P2) to create a health data

asset for her. Accordingly, the stakeholders can only

see the data assets that are shared with them or cre-

ated by them. Lastly, all the users/stakeholders can

view their coins only.

The validity of the ACL rules was checked on

both, the Hyperledger Composer-Playground and the

REST API. E.g., As shown in Figure-5a and Figure-

5b, to compare the access rights we have created a

user with admin rights that can view assets of all the

users (blood alcohol level), i.e., P5 and P6 in this case.

Whereas, the user P5 with ID Pid5 can see his assets

only. Moreover, Figure-5c and Figure-5d show that

initially, a user P4 with id Pid4 cannot see any asset,

as no asset is currently shared with him. However,

once user P5 shares his blood alcohol level with P4,

he can see P5's blood alcohol level. Similarly, only

P5 can initiate a TX to share its assets. Whereas, if P4

tries to share the asset of P5 with any other entity then

he will get an error (as shown in Figure-6) as he cur-

rently does not have the right to initiate a data sharing

TX.

3.2 Performance Efﬁciency

As far as the performance efﬁciency matters, the use

of multiple Chs to process different data types instead

of a single Ch facilitates simultaneous TX processing,

thus resulting in reduced network latency. Similarly,

Hyperledger-Fabric provides instant TX conﬁrmation

with varying throughput ranging from 1838 to 3560

TXs per second (Androulaki et al., 2018). The TX

throughput depends upon number of factors, such as,

block size, TX size, peer CPU, and number of peers

in the network. Moreover, Hyperledger-Fabric uses

Kafka consensus algorithm which is a voting-based

protocol, that provides consensus ﬁnality without any

risk of forks (Hyperledger, 2019). In addition, it has

very low computational and energy requirements as

compared to the PoW-based consensus protocols.

3.3 Limitations and Open Challenges

• Multiple Ledger Storage by the Peers. The

committing peers have to maintain multiple

ledgers, as per the number of channels they are

a member of. This can infer a massive resource

requirement for such nodes in a large smart city

network.

• IoT Device Integrity. IoT data, being the basic

element to provide various seamless services in

a smart city environment necessitates that the de-

vice initially generating and processing that data

should be credible, i.e., only a legitimate and

Figure 6: Validation of TX initiation rights.

clean device should be able to input data to the

blockchain. Whereas, currently there is no such

mechanism to test the integrity of the IoT devices

at run time.

4 CONCLUSIONS AND FUTURE

WORK

User data generated by today's smart devices rang-

ing from smart watches to smart cars, smart homes,

auto-pay systems, ITS, etc., is vulnerable to privacy

and security threats. Moreover, users also reserve

the right to manage and control access to the data

they own. Therefore, in this paper, we introduced

“PrivySharing,”, an innovative blockchain-based in-

tegrity and privacy-preserving data sharing mecha-

nism for smart cities. The proposed strategy ensures

that personal/critical user data is kept conﬁdential, se-

curely processed and is exposed to the stakeholders

on the need to know basis as per user-deﬁned ACL

rules embedded in the smart-contracts. Moreover, the

data owners are rewarded for sharing their data with

the stakeholders/third parties. PrivySharing also com-

plies with some of the signiﬁcant EU GDPR require-

ments.

Though we have presented all the details of the

proposed network architecture and security mecha-

nism, however, as a PoC for this paper, we imple-

mented a part of it. In the future, we aim to extend this

work and incorporate the concept of edge computing

to relieve end nodes from storing multiple ledgers.

We also plan to devise a mechanism for secure in-

tegration of IoT devices with the blockchain.

SECRYPT 2019 - 16th International Conference on Security and Cryptography

370

REFERENCES

Androulaki, E., Barger, A., Bortnikov, V., Cachin, C.,

Christidis, K., Caro, A. D., Enyeart, D., Ferris,

C., Laventman, G., Manevich, Y., Muralidharan, S.,

Murthy, C., Nguyen, B., Sethi, M., Singh, G., Smith,

K., Sorniotti, A., Stathakopoulou, C., Vukolic, M.,

Cocco, S. W., and Yellick, J. (2018). Hyperledger fab-

ric: A distributed operating system for permissioned

blockchains. CoRR, abs/1801.10228.

Bartoli, A., Hernández-Serrano, J., Soriano, M., Dohler,

M., Kountouris, A., and Barthel, D. (2011). Security

and privacy in your smart city. In Proceedings of the

Barcelona smart cities congress, volume 292.

Biswas, K. and Muthukkumarasamy, V. (2016). Securing

smart cities using blockchain technology. In Proceed-

ings of the 14th IEEE International Conference on

Smart City High Performance Computing and Com-

munications, pages 1392–1393.

Buterin, V. et al. (2014). A next-generation smart contract

and decentralized application platform. Whitepaper.

Cui, L., Xie, G., Qu, Y., Gao, L., and Yang, Y. (2018). Se-

curity and privacy in smart cities: Challenges and op-

portunities. IEEE Access, 6:46134–46145.

Dorri, A., Kanhere, S., Jurdak, R., and Gauravaram, P.

(2017). Blockchain for iot security and privacy: The

case study of a smart home. In Proceedings of the 2nd

IEEE Workshop on Security, Privacy, and Trust in the

Internet of Things (PERCOM), Hawaii, USA.

Dorri, A., Kanhere, S. S., and Jurdak, R. (2016). Blockchain

in internet of things: Challenges and solutions. arXiv

preprint arXiv:1608.05187.

GDPR (2018). General data protection regulation. Avail-

able at https://gdpr-info.eu/, Viewed 03 January 2019.

Haidar, F., Kaiser, A., and Lonc, B. (2017). On the per-

formance evaluation of vehicular pki protocol for v2x

communications security. In Proceedings of the 86th

IEEE Vehicular Technology Conference (VTC-Fall),

pages 1–5.

Huh, S., Cho, S., and Kim, S. (2017). Managing iot devices

using blockchain platform. In Proceedings of the 19th

International Conference on Advanced Communica-

tion Technology (ICACT), pages 464–467. IEEE.

Hyperledger (2019). Hyperledger architecture, volume

1. Available at https://www.hyperledger.org/wp-

content/uploads/2017/08/Hyperledger\_Arch\_WG\

_Paper\_1\_Consensus.pdf, Viewed 21 February

2019.

Kountché, D. A., Bonnin, J.-M., and Labiod, H. (2017). The

problem of privacy in cooperative intelligent trans-

portation systems (c-its). In Proceedings of IEEE

Conference on Computer Communications Workshops

(INFOCOM WKSHPS), pages 482–486.

Lund, D., MacGillivray, C., Turner, V., and Morales, M.

(2014). Worldwide and regional Internet of Things

(IoT) 2014–2020 forecast: A virtuous circle of proven

value and demand. International Data Corporation

(IDC), Tech. Rep.

Makhdoom, I., Abolhasan, M., Lipman, J., Liu, R. P., and

Ni, W. (2018). Anatomy of threats to the internet of

things. IEEE Communications Surveys Tutorials.

Makhdoom, I., Abolhasan, M., and Ni, W. (2018a).

Blockchain for iot: The challenges and a way forward.

In Proceedings of the 15th International Joint Confer-

ence on e-Business and Telecommunications - Volume

2: SECRYPT, pages 428–439. INSTICC, SciTePress.

Makhdoom, I., Abolhasan, M., and Ni, W. (2018b).

Blockchain’s adoption in iot: The challenges, and a

way forward. Journal of Network and Computer Ap-

plications, 125:251 – 279.

Mazhelis, O., Hämäläinen, A., Asp, T., and Tyrväinen, P.

(2016). Towards enabling privacy preserving smart

city apps. In International Smart Cities Conference

(ISC2), pages 1–7. IEEE.

Michelin, R. A., Dorri, A., Steger, M., Lunardi, R. C.,

Kanhere, S. S., Jurdak, R., and Zorzo, A. F. (2018).

Speedychain: A framework for decoupling data from

blockchain for smart cities. In Proceedings of the 15th

EAI International Conference on Mobile and Ubiqui-

tous Systems: Computing, Networking and Services,

pages 145–154. ACM.

Moustaka, V., Theodosiou, Z., Vakali, A., and Kounoudes,

A. (2018). Smart cities at risk!: Privacy and security

borderlines from social networking in cities. Athena,

357:905–910.

Puthal, D., Nepal, S., Ranjan, R., and Chen, J. (2016).

Threats to networking cloud and edge datacenters

in the internet of things. IEEE Cloud Computing,

3(3):64–71.

Rahman, M. A., Rashid, M. M., Hossain, M. S., Has-

sanain, E., Alhamid, M. F., and Guizani, M. (2019).

Blockchain and iot-based cognitive edge framework

for sharing economy services in a smart city. IEEE

Access, 7:18611–18621.

Sara, S. (2018). A Google bug exposed the informa-

tion of up to 500,000 users. Available at https:

//www.cnbc.com/2018/10/08/google-bug-exposed-

the-information-of-up-to-500000-users.html,

Viewed 15 February 2018.

Sara, S. and Michael, N. (2018). Facebook

has been worried about data leaks like this

since it went public in 2012. Available at

https://www.cnbc.com/2018/04/12/facebook-warned-

of-data-breaches-years-ago-when-it-went-public-in-

2012.html, Viewed 15 February 2018.

Sharma, P. K. and Park, J. H. (2018). Blockchain based

hybrid network architecture for the smart city. Future

Generation Computer Systems, 86:650–655.

Wurm, J., Hoang, K., Arias, O., Sadeghi, A.-R., and Jin, Y.

(2016). Security analysis on consumer and industrial

iot devices. In Proceedings of the 21st IEEE Asia and

South Paciﬁc Design Automation Conference (ASP-

DAC), pages 519–524.

Zhang, K., Ni, J., Yang, K., Liang, X., Ren, J., and Shen,

X. S. (2017). Security and privacy in smart city appli-

cations: Challenges and solutions. IEEE Communica-

tions Magazine, 55(1):122–129.

Zhang, Y. and Wen, J. (2016). The iot electric business

model: Using blockchain technology for the internet

of things. Peer-to-Peer Networking and Applications,

pages 1–12.

PrivySharing: A Blockchain-based Framework for Integrity and Privacy-preserving Data Sharing in Smart Cities

371